Im trying to create a RAG, i start by breaking down the document into chunks, send it to a localy hosted embedding model, get the vectors back, and then i get stuck with the FAISS part.
my problem is that all of what i find seems to want to connect to hugging face or something, and the langchain stuff seems to be geared towards doing that so i will get my embeddings from an external source or have to spend days re-running code until i get a model to download fully without telling me the hash doesnt match because it only tryed downloading 98.4% of it. why do that when i have a perfectly good server.
so please help me get on track, because ive been trying to find an answer for this for hours.
hears my proof of concept code

import requests
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import retrieval_qa, RetrievalQA
from langchain_community.vectorstores.faiss import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter

lm_studio_endpoint = "http://127.0.0.1:1234"

def setup_qa_system(file_path):
    # Load and split PDF documents
    try:
        loader = PyPDFLoader(file_path)
        docs = loader.load_and_split()
    except Exception as e:
        print(f"Error loading PDF: {e}")
        return None

    # Split documents into smaller chunks
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
    chunks = text_splitter.split_documents(docs)

    # Function to get embeddings
    def get_embeddings(texts):
        print(f"Getting embeddings for {len(texts)} texts.")  # Debug output
        try:
            response = requests.post(f"{lm_studio_endpoint}/v1/embeddings", json={
                "input": texts,
                'model': 'text-embedding-granite-embedding-278m-multilingual'
            })
            response.raise_for_status()
            embeddings_data = response.json().get('data')

            # Extract embeddings from the response
            embeddings = [item['embedding'] for item in embeddings_data]

            print(f"Received {len(embeddings)} embeddings.")  # Debug output
            return embeddings
        except requests.exceptions.RequestException as e:
            print(f"Error getting embeddings: {e}")
            return []

    texts = [chunk.page_content for chunk in chunks]  # Extract texts from chunks
    print(f"Number of chunks: {len(chunks)}")
    print(f"Number of texts being sent: {len(texts)}")

    embeddings = get_embeddings(texts)  # Get embeddings

    # Check if embeddings were retrieved successfully
    if not embeddings or len(embeddings) != len(chunks):
        print(f"Error: Number of embeddings ({len(embeddings)}) does not match number of chunks ({len(chunks)}). Exiting setup.")
        return None

    # Create a list of (text, embedding) tuples
    text_embeddings = list(zip(texts, embeddings))

    # Create the FAISS vector store using the list of tuples
    vector_store = FAISS.from_embeddings(text_embeddings, embeddings)# <<<------THIS IS WHERE IM GETTING STUCK

    retriever = vector_store.as_retriever()

    def query_local_model(question, context):
        try:
            response = requests.post(f"{lm_studio_endpoint}/v1/completions", json={
                "prompt": f"Question: {question}nContext: {context}nAnswer:",
                "max_tokens": 150
            })
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            print(f"Error querying LM Studio: {e}")
            return {}

    # Adjust the QA chain to use the local model query function
    qa_chain = RetrievalQA.from_chain_type(
        llm=query_local_model,
        retriever=retriever
    )
    return qa_chain

if __name__ == '__main__':
    qa_chain = setup_qa_system('Documents/OfMiceAndMen.pdf')

    if qa_chain:
        # Example query
        query = "What is the main theme of 'Of Mice and Men'?"
        relevant_chunks = qa_chain.retriever.retrieve(query)

        context = " ".join(chunk.text for chunk in relevant_chunks)  # Prepare context from retrieved chunks
        result = qa_chain.llm(query, context)  # Call the local model
        print(result)

”’

the output
”’

Number of chunks: 214
Number of texts being sent: 214
Getting embeddings for 214 texts.
Received 214 embeddings.
Traceback (most recent call last):
  File "MyProjectDirRagTestmain.py", line 83, in <module>
    qa_chain = setup_qa_system('Documents/OfMiceAndMen.pdf')
  File "MyProjectDirRagTestmain.py", line 59, in setup_qa_system
    vector_store = FAISS.from_texts(text_embeddings)
TypeError: FAISS.from_texts() missing 1 required positional argument: 'embedding'

”’
and a sample of the server log
”’

2024-12-27 13:13:47 [DEBUG] [INFO] [LlamaEmbeddingEngine] All parsed chunks succesfully embedded!
2024-12-27 13:13:47 [DEBUG] [INFO] [LlamaEmbeddingEngine] All parsed chunks succesfully embedded!
2024-12-27 13:13:47 [DEBUG] [INFO] [LlamaEmbeddingEngine] All parsed chunks succesfully embedded!
2024-12-27 13:13:47 [DEBUG] [INFO] [LlamaEmbeddingEngine] All parsed chunks succesfully embedded!
2024-12-27 13:13:47 [DEBUG] [INFO] [LlamaEmbeddingEngine] All parsed chunks succesfully embedded!
2024-12-27 13:13:47 [DEBUG] [INFO] [LlamaEmbeddingEngine] All parsed chunks succesfully embedded!
2024-12-27 13:13:47  [INFO] Returning embeddings (not shown in logs)

”’